近日，我室张甘霖研究员团队在美国土壤学会会刊（Soil Science Society of America Journal）上发表了关于三维数字土壤制图探索的研究工作。
三维土壤属性空间分布是进行土壤固碳估算、土壤肥力评估以及土壤污染防治等迫切需要的基础信息。然而，精细且准确的三维土壤信息却非常缺乏，当前的数字土壤制图方法主要针对表层土壤属性在二维水平方向上的空间变化而设计，难以直接用于进行三维土壤属性图。我室张甘霖研究员团队最近以皖南亚热带低山丘陵区为对象，采用将剖面深度函数与径基神经网络相结合的方法，在景观尺度上对土壤有机质的三维制图方法进行了探索研究。根据土壤发生层采样数据，采用剖面深度函数（幂函数、对数函数和二次样条函数）拟合土壤有机质含量的剖面垂直变化；根据土壤有机质与其形成环境条件（地形和土地利用等）之间的关系采用径基神经网络模拟土壤有机质含量的水平变化。研究发现，在灌丛地等土地利用方式下，幂函数和指数函数可以较好地拟合土壤有机质随着深度单调递减和渐变的剖面（比如灌丛地土壤），但是对于其它剖面变化复杂的情形，二次样条函数则表现出其灵活性，是一个更好的选择。三维有机质制图的精度随着深度增加而变低，进一步数据分析表明这主要与地形条件和土地利用方式对土壤有机质累积的影响随深度而变化有关。地形条件对土壤有机质的影响在上层60cm以内较强而在该深度以下较弱，土地利用方式则在上层30cm以内较强而在该深度以下较弱，地形条件和土地利用方式的影响在表层15cm均是最强的。在相同的地形条件下，农业耕作（水田或坡耕地）有助于上层30cm土壤有机质的累积。有关结果在美国土壤学会会刊（doi: 10.2136-sssaj2012.0317）发表。
Feng Liu Gan-Lin Zhang Yan-Jun Sun Yu-Guo Zhao De-Cheng Li. Mapping three-dimensional distribution of soil organic matter over a subtropical hilly landscape. SSSAJ 2013 doi:10.2136-sssaj2012.0317
Abstract
There is a serious lack of detailed and accurate three-dimensional (3D) soil distribution information worldwide. This paper examined the effectiveness of combining radial basis function (RBF) neural networks and profile depth functions to map 3D distribution of soil organic matter (SOM) in a subtropical hilly landscape in southern Anhui Province China. The RBF networks were used to predict lateral distribution of SOM based on its relations with terrain attributes and land uses while the depth functions were used to fit its vertical distribution based on sparse measurements of SOM in soil genetic horizons. Compared to power and logarithmic functions the equal-area quadratic splines had smaller bias higher accuracy and more stable performance in fitting vertical SOM distribution. The prediction accuracy of the whole 3D mapping method decreased with depth within the upper 60 cm and the best accuracy occurred below 60 cm. In the upper 30 cm areas with high elevation tended to have high predicted SOM content and vice-versa. There were local deviations from this pattern in areas where toeslopes and ravines had higher predicted SOM content than backslopes even though the latter are at higher elevations. Multiple regressions with dummies showed that the influence of terrain conditions on SOM content was strong in the upper 60 cm and weak below 60 cm while that of land use was strong in the upper 30 cm and weak below 30 cm. Both influences were the strongest in the 0-15 cm soil layer. Under the same terrain conditions agricultural cultivation is associated with SOM accumulation in the upper 30 cm.
